library(knitr)
opts_chunk$set(eval=TRUE, include=TRUE, cache=FALSE, cache.path='./__cache__/',
bootstrap.thumbnail.size= 'col-md-12', bootstrap.thumbnail = TRUE, bootstrap.show.code= FALSE,
fig.path='.', dev=c('png','pdf','svg','cairo_ps'), fig.width=8, fig.height=4, dpi=300)
knit_hooks$set(embed_fonts = function(before, options, envir) {
if ((!before) & (length(options['label'])>0)) {
old_path <- paste0(options['fig.path'], options['label'], '.pdf')
new_path <- paste0(options['fig.path'], options['label'], '-1.pdf')
if (file.exists(old_path)) {
embed_fonts(old_path, outfile = new_path)
}
NULL
}
})
# library(rmarkdown)
# render('political_affinity/pol_affn.Rmd', 'all', knit_root_dir='..')
library(data.table)
library(ggplot2)
library(ggridges)
library(ggrepel)
library(scales)
library(stats)
library(Hmisc)
# library(RColorBrewer)
library(car)
library(stargazer)
# library(doParallel)
# registerDoParallel(cores = 7)
library(arm)
library(glmnet)
library(pROC)
vars <- c("urls", "urls_panel", "urls_exp", "friendships", "panel")
if (length(setdiff(vars, ls(all.names = T)))>0) {
source("util/load_exp_data.R", chdir = T)
}
pol_act_l <- c("extreme left", "left", "center", "right", "extreme right",
"apolitical", "bot", "superconsumer", "supersharer")
# political exposure per user and site
user_site_exp <- urls_exp[domain_color_num>4,.(n_exp=.N), by=.(panel_uid, website)]
user_site_exp <- user_site_exp[, `:=`(
pct_site_exp=n_exp/sum(n_exp),
total_pol_exp=sum(n_exp)),
by=.(panel_uid)]
user_site_exp <- merge(x=user_site_exp,
y=panel[!is.na(party) & party %in% c("Republican", "Democrat") & is_outlier==F,
.(user_id, party)],
by.x = "panel_uid", by.y = "user_id", all.x = T)
# site_align <- fread("political_affinity/site_align.tsv")
align_exp <- user_site_exp[!is.na(party) & total_pol_exp>=100 & pct_site_exp>0.01,]
align_exp <- align_exp[, `:=`(n_uniq_users=.N), by=.(website)]
nd <- length(unique(align_exp[n_uniq_users>30 & party=="Democrat"]$panel_uid))
nr <- length(unique(align_exp[n_uniq_users>30 & party=="Republican"]$panel_uid))
wd <- 0.5*(nd+nr)/nd
wr <- 0.5*(nd+nr)/nr
site_align_rep <- align_exp[n_uniq_users>30,
.(n=.N,
n_rep=sum(party=="Republican"),
n_dem=sum(party=="Democrat")), by=.(website)]
site_align_rep <- site_align_rep[,`:=`(
#align_raw = 2*n_rep/n-1,
align = 2*wr*n_rep/(wd*n_dem+wr*n_rep)-1
)]
site_align <- site_align_rep[,.(website, align)]
# Using Facebook's top 500 sites from Science paper
fb_domain_align <- fread('restricted_data/fb_top500_domain_affl.csv', showProgress = F,
select = c("domain", "avg_align"))
fb_domain_align <- unique(fb_domain_align, by=c("domain"))
fb_domain_align <- merge(x=fb_domain_align, y=site_align, by.x = "domain",
by.y = "website", all.x = T)[order(avg_align)]
print(sprintf("%d/%d sites overlaped with Bakshy et al.'s list",
nrow(fb_domain_align[!is.na(align)]), nrow(fb_domain_align) ))
[1] "109/495 sites overlaped with Bakshy et al.'s list"
print(cor.test(fb_domain_align[!is.na(align)]$avg_align,
fb_domain_align[!is.na(align)]$align,
method = "pearson", conf.level = 0.95))
Pearson's product-moment correlation
data: fb_domain_align[!is.na(align)]$avg_align and fb_domain_align[!is.na(align)]$align
t = 22.578, df = 107, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.8697598 0.9369938
sample estimates:
cor
0.9091256
ggplot(data = fb_domain_align[!is.na(align),],
aes(x=avg_align, y=align )) +
geom_point() +
scale_x_continuous(expand=c(0,0), limits=c(-1,1)) +
scale_y_continuous(expand=c(0,0), limits=c(-1,1)) +
geom_smooth(data=fb_domain_align[!is.na(align),], method='lm',formula=y~x, fullrange=T) +
geom_abline(slope = 1, intercept = 0) +
geom_text_repel(aes(label=domain), size=1) +
labs(y="Site Alignment", x="Alignment (Bakshy et al.)") +
theme(axis.line.x=element_blank())

budak <- data.table(
website=c(
"dailykos.com", "nytimes.com", "huffingtonpost.com", "latimes.com",
"washingtonpost.com", "bbcnews.com", "cnn.com", "yahoo.com", "reuters.com",
"nbcnews.com", "chicagotribune.com", "usatoday.com", "wsj.com",
"foxnews.com", "breitbart.com"),
slant=c(-0.24, -0.054, -0.05, -0.04,
-0.01, -0.01, -0.005, -0.005, 0,
0.005, 0.01, 0.11, 0.06,
0.11, 0.17))
budak <- merge(x=budak, y=site_align, by = "website", all.x = T)[order(slant)]
budak_m <- budak[!is.na(align),]
print(sprintf("%d/%d sites overlaped with Budak et al.'s list",
nrow(budak_m), nrow(budak) ))
[1] "14/15 sites overlaped with Budak et al.'s list"
print(cor.test(budak_m$slant,budak_m$align, method = "pearson", conf.level = 0.95))
Pearson's product-moment correlation
data: budak_m$slant and budak_m$align
t = 6.8173, df = 12, p-value = 1.857e-05
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.6848938 0.9654257
sample estimates:
cor
0.8915077
print(budak_m[order(slant),.(website, slant, align)])
website slant align
1: dailykos.com -0.240 -0.606063164
2: nytimes.com -0.054 -0.155912285
3: huffingtonpost.com -0.050 -0.337856555
4: latimes.com -0.040 -0.258046270
5: washingtonpost.com -0.010 -0.132981727
6: cnn.com -0.005 -0.071515112
7: yahoo.com -0.005 0.005735093
8: reuters.com 0.000 -0.083953557
9: nbcnews.com 0.005 -0.089164047
10: chicagotribune.com 0.010 -0.127352043
11: wsj.com 0.060 0.079104664
12: foxnews.com 0.110 0.685500953
13: usatoday.com 0.110 0.093322588
14: breitbart.com 0.170 0.603952822
user_align_sites <- merge(x=user_site_exp,
y=site_align[,.(website, align)],
by = "website")
user_align <- user_align_sites[,.(pol_align=weighted.mean(align, n_exp)),
by=.(panel_uid)]
imp_data <- merge(x=panel[,.(user_id, perc_obama, party, is_outlier, pol_affl)], y=user_align, by.x = "user_id", by.y = "panel_uid", all.x = T)
imp_data <- imp_data[, party_dv:=factor(party, c("Democrat", "Republican"))]
imp_data <- imp_data[, party:=NULL]
imp_no_na <- imp_data[!is.na(party_dv) & is_outlier==F,]
# train on registered voters, predict continous score for everyone
f_rest <- ~ perc_obama + pol_align
m1 <- cv.glmnet(as.matrix(model.matrix(f_rest, imp_no_na[!is.na(pol_align)])),
imp_no_na[!is.na(pol_align)]$party_dv, family="binomial", alpha = 0,
# original dataset had slightly different mean so offseting now
offset = rep(-0.17, nrow(imp_no_na[!is.na(pol_align)]))
)
imp_data <- imp_data[!is.na(pol_align), party_score:=
as.vector(predict(m1, newx = as.matrix(model.matrix(f_rest, imp_data[!is.na(pol_align)])),
s = "lambda.min", type = "response", newoffset = 0))]
# train a second model based on just the perc_obama for cases where pol_align is NA
m2 <- cv.glmnet(as.matrix(model.matrix(~ perc_obama, imp_no_na)),
imp_no_na$party_dv, family="binomial", alpha = 0, offset = rep(-0.38, nrow(imp_no_na)))
imp_data <- imp_data[is.na(pol_align), party_score:=
as.vector(predict(m2, newx = as.matrix(model.matrix(~ perc_obama, imp_data[is.na(pol_align)])),
s = "lambda.1se", type = "response", newoffset = 0))]
imp_data <- imp_data[, pol_affl_rep:=2*party_score-1]
print(sprintf("99%% of reproduced affiliation scores are less than this much off: %2.3f",
quantile(abs(imp_data$pol_affl-imp_data$pol_affl_rep), 0.99)))
[1] "99% of reproduced affiliation scores are less than this much off: 0.008"
print(auc(imp_data[!is.na(party_dv),]$party_dv, imp_data[!is.na(party_dv),]$party_score)) # 0.8074
Area under the curve: 0.8073
plot_df <- panel[is_compromised==F & is_bot==F & pol_affl_act!="apolitical",]
lvls_rev <- function(f) factor(f, rev(levels(f)))
plot_lbls <- plot_df[,.(
lbl=sprintf("N=%d, %2.0f%%", .N, 100*sum(n_fn_exp<1)/.N), exp_rate=median(exp_rate[n_fn_exp>0])),
by=pol_affl_act]
ggplot(plot_df[n_fn_exp>0], aes(x=exp_rate, y=lvls_rev(pol_affl_act) )) +
geom_density_ridges_gradient(
aes(fill = ..x..), scale = 2, size = 0.3
) +
scale_fill_gradientn(
colours = c("#0D0887FF", "#CC4678FF", "#F0F921FF"),
guide = "none"
) +
# geom_text(data = plot_lbls, aes(label=nz_pct), size=4, colour="black", vjust = -2, nudge_x = -0.3, hjust = 0.5) +
geom_text(data = plot_lbls, aes(label=lbl, x=0.00015), size=4, colour="black", vjust = -3.5, hjust = 0) +
scale_x_log10(labels=percent, breaks = c(0.001, 0.01, 0.1), limits = c(0.0001, 0.6), expand = c(0, 0) ) +
scale_y_discrete(expand = c(0.04, 0)) +
annotation_logticks(sides = "b") +
labs(x="Fraction of fake news", y = NULL)
Picking joint bandwidth of 0.162

---
title: "Calculate political affinity scores"
author: "Nir Grinberg"
date: "12/31/2018"
output:
  html_notebook:
    top: yes
  md_document:
    toc: yes
editor_options: 
  chunk_output_type: inline
---

```{r global_settings}
library(knitr)
opts_chunk$set(eval=TRUE, include=TRUE, cache=FALSE, cache.path='./__cache__/',
               bootstrap.thumbnail.size= 'col-md-12', bootstrap.thumbnail = TRUE, bootstrap.show.code= FALSE, 
               fig.path='.', dev=c('png','pdf','svg','cairo_ps'), fig.width=8, fig.height=4, dpi=300)

knit_hooks$set(embed_fonts = function(before, options, envir) {
  if ((!before) & (length(options['label'])>0)) {
    old_path <- paste0(options['fig.path'], options['label'], '.pdf')
    new_path <- paste0(options['fig.path'], options['label'], '-1.pdf')
    if (file.exists(old_path)) {
        embed_fonts(old_path, outfile = new_path)
    }
    NULL
  }
})
# library(rmarkdown)
# render('political_affinity/pol_affn.Rmd', 'all', knit_root_dir='..')
```

```{r imports, eval=TRUE, echo=TRUE, results='hide', warning=FALSE, error=FALSE, message=FALSE}
library(data.table)
library(ggplot2)
library(ggridges)
library(ggrepel)
library(scales)
library(stats)
library(Hmisc)
# library(RColorBrewer)
library(car)
library(stargazer)
# library(doParallel)
# registerDoParallel(cores = 7)
library(arm)
library(glmnet)
library(pROC)
```

```{r load_exp_data}
vars <- c("urls", "urls_panel", "urls_exp", "friendships", "panel")
if (length(setdiff(vars, ls(all.names = T)))>0) {
  source("util/load_exp_data.R", chdir = T)
}
pol_act_l <- c("extreme left", "left", "center", "right", "extreme right", 
               "apolitical", "bot", "superconsumer", "supersharer")
# political exposure per user and site
user_site_exp <- urls_exp[domain_color_num>4,.(n_exp=.N), by=.(panel_uid, website)]
user_site_exp <- user_site_exp[, `:=`(
  pct_site_exp=n_exp/sum(n_exp),
  total_pol_exp=sum(n_exp)), 
  by=.(panel_uid)]
user_site_exp <- merge(x=user_site_exp, 
                       y=panel[!is.na(party) & party %in% c("Republican", "Democrat") & is_outlier==F,
                               .(user_id, party)],
                       by.x = "panel_uid", by.y = "user_id", all.x = T)
```

```{r compute_site_align}
# site_align <- fread("political_affinity/site_align.tsv")
align_exp <- user_site_exp[!is.na(party) & total_pol_exp>=100 & pct_site_exp>0.01,]
align_exp <- align_exp[, `:=`(n_uniq_users=.N), by=.(website)]
nd <- length(unique(align_exp[n_uniq_users>30 & party=="Democrat"]$panel_uid))
nr <- length(unique(align_exp[n_uniq_users>30 & party=="Republican"]$panel_uid))
wd <- 0.5*(nd+nr)/nd
wr <- 0.5*(nd+nr)/nr
site_align_rep <- align_exp[n_uniq_users>30,
                            .(n=.N,
                              n_rep=sum(party=="Republican"),
                              n_dem=sum(party=="Democrat")), by=.(website)]
site_align_rep <- site_align_rep[,`:=`(
  #align_raw = 2*n_rep/n-1,
  align     = 2*wr*n_rep/(wd*n_dem+wr*n_rep)-1
)]
site_align <- site_align_rep[,.(website, align)]
```

```{r compare_with_bakshy_etal}
# Using Facebook's top 500 sites from Science paper
fb_domain_align <- fread('restricted_data/fb_top500_domain_affl.csv', showProgress = F, 
                         select = c("domain", "avg_align"))
fb_domain_align <- unique(fb_domain_align, by=c("domain"))
fb_domain_align <- merge(x=fb_domain_align, y=site_align, by.x = "domain", 
                         by.y = "website", all.x = T)[order(avg_align)]
print(sprintf("%d/%d sites overlaped with Bakshy et al.'s list", 
              nrow(fb_domain_align[!is.na(align)]), nrow(fb_domain_align) )) 
print(cor.test(fb_domain_align[!is.na(align)]$avg_align, 
               fb_domain_align[!is.na(align)]$align, 
               method = "pearson", conf.level = 0.95))

ggplot(data = fb_domain_align[!is.na(align),], 
            aes(x=avg_align, y=align )) + 
  geom_point() +
  scale_x_continuous(expand=c(0,0), limits=c(-1,1)) +
  scale_y_continuous(expand=c(0,0), limits=c(-1,1)) +
  geom_smooth(data=fb_domain_align[!is.na(align),], method='lm',formula=y~x, fullrange=T) + 
  geom_abline(slope = 1, intercept = 0) + 
  geom_text_repel(aes(label=domain), size=1) +
  labs(y="Site Alignment", x="Alignment (Bakshy et al.)") + 
  theme(axis.line.x=element_blank())
```

```{r compare_with_budak_etal}
budak <- data.table(
  website=c(
    "dailykos.com", "nytimes.com", "huffingtonpost.com", "latimes.com", 
    "washingtonpost.com", "bbcnews.com", "cnn.com", "yahoo.com", "reuters.com",
    "nbcnews.com", "chicagotribune.com", "usatoday.com", "wsj.com", 
    "foxnews.com", "breitbart.com"), 
  slant=c(-0.24, -0.054, -0.05,  -0.04, 
          -0.01, -0.01,  -0.005, -0.005, 0, 
          0.005,  0.01,   0.11,   0.06,  
          0.11,   0.17))
budak <- merge(x=budak, y=site_align, by = "website", all.x = T)[order(slant)]
budak_m <- budak[!is.na(align),]
print(sprintf("%d/%d sites overlaped with Budak et al.'s list", 
              nrow(budak_m), nrow(budak) )) 
print(cor.test(budak_m$slant,budak_m$align, method = "pearson", conf.level = 0.95))
print(budak_m[order(slant),.(website, slant, align)])
```

```{r compute_user_align_scores}
user_align_sites <- merge(x=user_site_exp,
                          y=site_align[,.(website, align)],
                          by = "website")
user_align <- user_align_sites[,.(pol_align=weighted.mean(align, n_exp)), 
                               by=.(panel_uid)]
```

```{r compute_pol_affl}
imp_data <- merge(x=panel[,.(user_id, perc_obama, party, is_outlier, pol_affl)], y=user_align, by.x = "user_id", by.y = "panel_uid", all.x = T)
imp_data <- imp_data[, party_dv:=factor(party, c("Democrat", "Republican"))]
imp_data <- imp_data[, party:=NULL]
imp_no_na <- imp_data[!is.na(party_dv) & is_outlier==F,]

# train on registered voters, predict continous score for everyone
f_rest <- ~ perc_obama + pol_align 
m1 <- cv.glmnet(as.matrix(model.matrix(f_rest, imp_no_na[!is.na(pol_align)])), 
                imp_no_na[!is.na(pol_align)]$party_dv, family="binomial", alpha = 0,
                # original dataset had slightly different mean so offseting now
                offset = rep(-0.17, nrow(imp_no_na[!is.na(pol_align)])) 
                )
imp_data <- imp_data[!is.na(pol_align), party_score:=
                       as.vector(predict(m1, newx = as.matrix(model.matrix(f_rest, imp_data[!is.na(pol_align)])), 
                                         s = "lambda.min", type = "response", newoffset = 0))]

# train a second model based on just the perc_obama for cases where pol_align is NA
m2 <- cv.glmnet(as.matrix(model.matrix(~ perc_obama, imp_no_na)), 
                imp_no_na$party_dv, family="binomial", alpha = 0, offset = rep(-0.38, nrow(imp_no_na)))
imp_data <- imp_data[is.na(pol_align), party_score:=
                       as.vector(predict(m2, newx = as.matrix(model.matrix(~ perc_obama, imp_data[is.na(pol_align)])), 
                                         s = "lambda.1se", type = "response", newoffset = 0))]
imp_data <- imp_data[, pol_affl_rep:=2*party_score-1]

print(sprintf("99%% of reproduced affiliation scores are less than this much off: %2.3f", 
              quantile(abs(imp_data$pol_affl-imp_data$pol_affl_rep), 0.99)))
print(auc(imp_data[!is.na(party_dv),]$party_dv, imp_data[!is.na(party_dv),]$party_score)) # 0.8074
```

```{r fn_exp_dist_by_pol_affl, fig.width=8, fig.height=8}
plot_df <- panel[is_compromised==F & is_bot==F & pol_affl_act!="apolitical",]
lvls_rev <- function(f) factor(f, rev(levels(f)))
plot_lbls <- plot_df[,.(
                    lbl=sprintf("N=%d, %2.0f%%", .N, 100*sum(n_fn_exp<1)/.N), exp_rate=median(exp_rate[n_fn_exp>0])), 
                    by=pol_affl_act]
ggplot(plot_df[n_fn_exp>0], aes(x=exp_rate, y=lvls_rev(pol_affl_act) )) + 
  geom_density_ridges_gradient(
    aes(fill = ..x..), scale = 2, size = 0.3
  ) +
  scale_fill_gradientn(
    colours = c("#0D0887FF", "#CC4678FF", "#F0F921FF"),
    guide = "none"
  ) +
  # geom_text(data = plot_lbls, aes(label=nz_pct), size=4, colour="black", vjust = -2, nudge_x = -0.3, hjust = 0.5) +
  geom_text(data = plot_lbls, aes(label=lbl, x=0.00015), size=4, colour="black", vjust = -3.5, hjust = 0) +
  scale_x_log10(labels=percent, breaks = c(0.001, 0.01, 0.1), limits = c(0.0001, 0.6), expand = c(0, 0) ) +
  scale_y_discrete(expand = c(0.04, 0)) +
  annotation_logticks(sides = "b") +
  labs(x="Fraction of fake news", y = NULL)
```